1
$\begingroup$

I am using a hurricane dataset (specifically the NDAM and Gender_MF columns):

set.seed(100)
library(ggplot2)
library(rio)
url = "https://www.pnas.org/highwire/filestream/616321/field_highwire_adjunct_files/0/pnas.1402786111.sd01.xlsx"
data = rio::import(url)
data = data[1:92,]
ggplot(data = data, mapping = aes(x = NDAM, fill = factor(Gender_MF), 
       color = factor(Gender_MF))) + geom_density(alpha = 1/20, adjust = 1/2)

Plot

Both distributions are skewed and need transformation.

My aim is to fit a model and see whether Gender_MF explains the hurricane damage. So, I consider the NDAM as a count and fitted a Poisson regression as follows.

pois.reg <- glm(NDAM ~ factor(Gender_MF), family = poisson, data = data)

The summary output enter image description here

Does this Poisson regression model have a good fit for these data? How can I interpret the coefficients? Can I say Gender_MF explains the hurricane damage?

$\endgroup$
  • $\begingroup$ What is "NDAM"? More generally, what are these data? What paper are they from? I'm pretty sure they simply alternate between male & female names in giving names to storms. Is your hypothesis that stronger & weaker storms alternate? $\endgroup$ – gung - Reinstate Monica Jul 19 at 19:03
  • 2
    $\begingroup$ @gung the data was taken from pnas.org/content/early/2014/05/29/1402786111/tab-figures-data and NDAM is the normalized damage of the hurricanes. I was trying to explain if Gender_MF explains the damage without including other explanatory variables. $\endgroup$ – Matthew Jul 19 at 19:08
  • $\begingroup$ The links I left in your last question about this paper have re-analysis of this dataset that go into a great deal of detail and come to firmer conclusions than we can based on this output alone. $\endgroup$ – mkt - Reinstate Monica Jul 19 at 19:10
  • $\begingroup$ @mkt I read all the criticisms on the paper. My aim, as a beginner, is to learn about selecting and fitting appropriate regression models. I started by fitting a Poisson regression to see if gender explains the hurricane damage. I also tried gam() and glm() but I thought Poisson regression is better. $\endgroup$ – Matthew Jul 19 at 19:28
  • 1
    $\begingroup$ Note that Joseph Hilbe is the statistician on the paper. They used negative binomial regression, not Poisson, AFAICT. I can't seem to find a definition of normalized in "normalized damage"; I'm not sure what that means, but they do seem to clearly state that these are counts. $\endgroup$ – gung - Reinstate Monica Jul 19 at 19:37
3
$\begingroup$

I'm ignoring external context about this paper and analysis for the purposes of this answer.

1. Does this Poisson regression model have a good fit for these data?

We have no way to judge that from the output you have presented.

2. How can I interpret the coefficients?

I don't know which genders 0 and 1 represent. But the output means that

Gender_MF = 0 has an expected NDAM of exp(8.936) = 7600

Gender_MF = 1 has an expected NDAM of exp(8.936 - 0.068) = 7100

So Gender_MF = 1 is associated with a 500 unit decrease in NDAM relative to Gender_MF = 0.

Could be worth your time to read How to interpret coefficients in a Poisson regression?

3. Can I say Gender_MF explains the hurricane damage?

I would say instead that Gender_MF is associated with hurricane damage in this dataset, conditional on a set of assumptions that we cannot evaluate from the model output alone. 'Explains' is a bit ambiguous but hints at a causal claim, and I would be very wary of making causal claims based on this alone.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.